Wearable ambulatory data recorder

ABSTRACT

A wearable ambulatory data recorder that senses physiological parameters of a patient, and stores physiological parameter data for later retrieval, as well as techniques for using such a wearable ambulatory data recorder, are described. The data recorder includes one or more sensors located on or within a housing. The data recorder may include an adhesive layer for attachment to a patient. In some embodiments, the housing may be within a patch, e.g., bandage, which includes the adhesive layer. The housing may be waterproof. Features of the data recorder such as size, waterproofness, and inclusion of an adhesive may allow the data recorder to be unobtrusively worn by a patient during a variety of daily activities. The data recorder may be for single use and thereafter disposable.

This application is a continuation of U.S. application Ser. No.11/413,619, filed Apr. 28, 2006, which claims the benefit of U.S.provisional application no. 60/742,002, filed Dec. 2, 2005, and U.S.provisional application no. 60/785,657, filed Mar. 24, 2006. The entirecontent of each of these three applications is incorporated by referenceherein.

TECHNICAL FIELD

The invention relates to medical devices and, more particularly, tomedical devices that monitor physiological parameters.

BACKGROUND

The quality of a patient's life may be an indication of the severity orprogression of a symptom or aliment, and also of the efficacy of atherapy to treat the symptom or ailment. For example, quality of life isdirectly related to the level of pain a patient experiences in everydaylife. Accordingly, the quality of the patient's life may indicate theseverity of the pain and effectiveness of therapies to treat the pain.The quality of a patient's life may similarly be impacted by: movementdisorders, such as tremor, Parkinson's disease, spasticity, and multiplesclerosis; psychological disorders, such as depression, mania, bipolardisorder, or obsessive-compulsive disorder; cardiac disorders, such ascongestive heart failure or arrhythmia; gastric disorders, such asgastroparesis; or obesity.

The quality of sleep experienced by a patient, the overall activitylevel of a patient, or the amount of time a patient spends engaged inparticular activities, in particular postures, or above particularactivity levels may indicate the quality of the patient's life, andthereby the effectiveness of a therapy used to treat a symptom ordisorder, such as those identified above. However, such life qualitymetrics are not typically quantified for evaluation of disease state ortherapy. For example, a major limitation of most of the pain literatureis the poor assessment of sleep conducted in many studies. There arerelatively few objective polysomnographic and actigraphic assessments,and most studies rely solely on retrospective subjective self-reportmeasures of sleep disturbance.

Chronic pain may cause a patient to avoid particular activities, oractivity in general, where such activities increase the pain experiencedby the patient. When a patient is inactive, he or she may be more likelyto be recumbent, i.e., lying down, or sitting, and may change posturesless frequently. Additionally, sleep disturbance is perhaps one of themost prevalent complaints of patients with chronically painfulconditions. Quality of sleep, quantity of sleep, and trouble fallingasleep may be related to the intensity and frequency of pain.

Similarly, the difficulty walking or otherwise moving experienced bypatients with movement disorders may cause such patients to avoidparticular activities or posture, or movement in general, to the extentpossible. Further, the uncontrolled movements associated with suchmovement disorders may cause a patient to have difficulty fallingasleep, disturb the patient's sleep, or cause the patient to havedifficulty achieving deeper sleep states. Additionally, manypsychological disorders disturb a patient's sleep, and cause them toengage in less activity during the day. Patients with depression oftenspend much of the day in bed or otherwise recumbent.

Further, in some cases, poor sleep quality may increase the symptomsexperienced by a patient due to an ailment. For example, poor sleepquality has been linked to increased pain symptoms in chronic painpatients, due to lowering the pain threshold of the patient. Poor sleepmay similarly increase tremor in movement disorder patients or the levelof symptoms for some psychological disorders. The link between poorsleep quality and increased symptoms is not limited to ailments thatnegatively impact sleep quality, such as those listed above.Nonetheless, the condition of a patient with such an ailment mayprogressively worsen when symptoms disturb sleep quality, which in turnincreases the frequency and/or intensity of symptoms. The increasedsymptoms may, in turn, limit patient activity during the day, andfurther disturb sleep quality.

SUMMARY

In general, the invention is directed to a wearable ambulatory datarecorder that senses physiological parameters of a patient, and storesphysiological parameter data based on the sensed parameters for laterretrieval, as well as techniques for using such a wearable ambulatorydata recorder. The data recorder includes one or more sensors located onor within a housing, where a sensor may detect a physiological parametersuch as heart activity, brain activity, temperature, tissue oxygenation,activity, or posture. Data may be recorded for a time period of a fewminutes to multiple days to provide objective data regarding the qualityof life of a patient, e.g., the quality of sleep, or degree of activityor postural changes, of the patient. Such data may be used, as examples,for patient monitoring or diagnosis, to provide closed-loop feedback fora therapy, or to evaluate the effectiveness of a therapy delivered tothe patient. The data may be transferred to a computer forpost-processing and review.

The size of the data recorder may allow it to be worn in an unobtrusivemanner that does not interfere with patient activities. As examples, thethickness of the housing of the data recorder may be approximately 0.6centimeters. Further, in some embodiments, a volume of the housing maybe approximately 6.3 cubic centimeters. The data recorder may include anadhesive layer for attachment to a patient. In some embodiments, thehousing may be within a patch, which may be bandage-like, that includesthe adhesive layer. The housing may be waterproof to allow the patientto continue normal daily activities that include physical activity,swimming, or showing. Additional features of the data recorder mayinclude inexpensive manufacturing which may allow the data recorder tobe disposable after patient use.

Data stored by the data recorder may be used to diagnose patientconditions, to provide closed-loop feedback for therapies, or to monitorpatient response to received therapies. For example, the data recordermay be used to gather data before, during, or after stimulation or drugtherapy to monitor any changes in patient activity or physiologicalparameters. In some embodiments, the data recorder, a medical device, ora separate computing device associates physiological parameter datagathered by the data recorder with a plurality of therapy parameter setsused by the medical device to control delivery to a patient over time.

In this manner, a user may compare the relative efficacy of the therapyparameter sets by comparing the physiological parameter data associatedwith the therapy parameter sets. The data recorder may record dataduring delivery of therapy by an implantable medical device according toa plurality of parameter sets during normal use, e.g., as the patientselects and modifies parameter sets. As another example, the datarecorder may record physiological parameter data during a “trialing”phase of a therapy, such as neurostimulation for treatment of pain ormovement disorders, during which a plurality of therapy parameter setsare trialed over a relatively short period of time. During the trialingphase, therapy may be delivered by an implantable medical device, or anexternal trial device, in which cast the physiological parameter datamay be used to assist in the identification of efficacious parametersets for eventual programming of the implanted medical device.

In some embodiments, the data recorder, medical device, or othercomputing device may further process the physiological parameter data todetermine values for one or more metrics indicative of the quality of apatient's life. As examples, sleep quality, activity, or posture metricsmay be determined. Metrics indicative of sleep quality may include sleepefficiency, sleep latency, and time spent in deeper sleep states, e.g.,one or both of the S3 and S4 sleep state. Activity metrics may includepercentages or lengths of time above a threshold activity level, whileposture metrics may include numbers of posture transitions, or length orpercentage of time spent in particular postures, e.g., upright. Suchmetric values may be determined for each of a plurality of therapyparameter sets based on the physiological parameter data associated withthe parameter sets, and presented to a user to allow evaluation,comparison, and selection of therapy parameter sets.

In embodiments in which the wearable ambulatory data recorder includes aposture sensor, e.g., a multi-axis accelerometer, the invention providestechniques for calibrating the sensor. In particular, it may bedesirable to calibrate the sensor by having the patient assume apredetermined posture, e.g., upright, and then signaling the recorderthat the patient is in the predetermined posture. Accordingly, the datarecorder identifies a current posture of the patient in response to thesignal, and associates that posture with the predetermined posture tocalibrate the sensor.

The data recorder may include a removable element, e.g., attached to thehousing of the data recorder, and may detect removal of the element asthe signal to calibrate the sensor, e.g., as the signal that the patientis in the predetermined posture. In some embodiments, removal of theelement also powers on the data recorder, which calibrates the sensor inresponse to being powered-on. The element may be, as an example, amagnet, which may be attached to the data recorder by an adhesive. Insome embodiments, the magnet is included in a backing layer for anadhesive that the attaches the data recorder to the patient, or adifferent backing layer, attached by a different adhesive, that isremoved after attachment of the data recorder to the patient.

In one embodiment, the disclosure provides an external wearableambulatory data recorder comprising a housing, a sensor that generates asignal as a function of posture of a patient that wears the datarecorder within the housing, a memory within the housing, a processorwithin the housing that receives signal from the sensor, and storesposture data within the memory for the patient based on the signal, andan element removably attached to the data recorder. The processorcalibrates the sensor in response to removal of the element.

In another embodiment, the disclosure provides an external wearableambulatory data recorder comprising a housing, means within the housingfor sensing posture of a patient that wears the data recorder andgenerating posture data, means within the housing for storing theposture data, an element removably attached to the housing, and meansfor calibrating the posture sensing means in response to removal of theelement.

In another embodiment, the disclosure provides a method comprisingsensing posture of a patient via an external ambulatory data recorderworn by the patient, storing posture data within a memory of the datarecorder, detecting removal of an element from the data recorder, andcalibrating posture sensing in response to the removal.

In another embodiment, the disclosure provides a method comprisingdelivering a therapy to a patient via a medical device, sensing aplurality of physiological parameters of the patient via an externalwearable ambulatory data recorder during delivery of the therapy, thedata recorder separate from the medical device, and providing thephysiological parameter data to a user for evaluation of the therapy,the physiological parameter data determined based on the sensedphysiological parameters.

In another embodiment, the disclosure provides a system comprising amedical device that delivers a therapy to a patient a separate externalwearable ambulatory data recorder that senses a plurality ofphysiological parameters of the patient during delivery of the therapy,and stores physiological parameter data based on the sensedphysiological parameters, and a processor that provides thephysiological parameter data to a user for evaluation of the therapy.

In another embodiment, the disclosure provides a system comprising meansfor delivering a therapy to a patient, separately housing external meansfor sensing a plurality of physiological parameters of the patientduring delivery of the therapy means for determining physiologicalparameter data based on the sensed physiological parameters, and meansfor providing the physiological parameter data to a user for evaluationof the therapy.

In other embodiments, the invention is directed to computer-readablemedia containing instructions that cause a programmable processor toperform any one or more of the methods and techniques described herein.

Embodiments of the invention may provide one or more advantages. Forexample, a data recorder according to the invention may provide aphysician with objective data related to patient activity and patientquality of life, rather than subjective recollections from the patient.The data recorder may also eliminate bulky or tethered data recordingsystems that limit patient activity and contribute to patient awarenessof monitoring. In addition, a small, wearable, and waterproof datarecorder may provide a simple disposable solution to patient monitoringthat may be beneficial to a wide variety of health care applications. Insome embodiments, calibration of a posture sensor of the data recordermay be relatively easy in that it may involve removing a single itemfrom the recorder.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating an example wearableambulatory data recorder (ADR) attached to a patient at an examplelocation.

FIG. 2 is perspective view of the wearable ADR of FIG. 1.

FIG. 3A is a cross-sectional side-view further illustrating the wearableADR of FIG. 1, the cross section taken along line A-A′ of FIG. 2.

FIG. 3B is a cross-sectional top-view further illustrating the wearableADR of FIG. 1, the cross section taken along line B-B′ of FIG. 3A.

FIG. 3C is a cross-sectional side-view illustrating another examplewearable ADR.

FIG. 4 is block diagram illustrating an example system that includes thewearable ADR of FIG. 1, and further illustrating the wearable ADR ofFIG. 1.

FIG. 5 is a flow diagram illustrating an example method for operating awearable ADR according to the invention.

FIG. 6 is a graph illustrating an example relationship between postureand activity.

FIG. 7 is a timing diagram illustrating example physiological parameterdata collected by a wearable ADR.

FIG. 8 is a histogram illustrating an example presentation ofphysiological parameter data collected by a wearable ADR.

FIG. 9 is a conceptual diagram illustrating the wearable ADR and patientof FIG. 1 in conjunction with an implantable medical device implantedwithin the patient.

FIG. 10 is a flow diagram illustrating example use of physiologicalparameter data collected by a wearable ADR.

FIG. 11 is a flow diagram illustrating an example technique forassociating physiological parameter data collected by a wearable ADRwith therapy data relating to therapy delivered by a medical device.

FIG. 12 is a flow diagram illustrating an example technique fordetermining quality of life metric values for each of a plurality oftherapy parameter sets used by a medical device that delivers therapybased on physiological parameter data collected by a wearable ADR.

FIGS. 13-15 are conceptual diagrams illustrating presentation of variousexample quality of life metric values associated with therapy parametersets to a user.

DETAILED DESCRIPTION

FIG. 1 is a conceptual diagram illustrating an example wearableambulatory data recorder (ADR) 10 attached to a patient 12 at an examplelocation. ADR 10 includes at least one sensor that senses aphysiological parameter of a patient, and may include a variety ofsensors (not shown in FIG. 1) to sense a variety of physiologicalparameters of the patient, as will be described in greater detail below.ADR 10 may store physiological parameter data determined based on thesensed physiological parameters for later retrieval and presentation toa user. In this manner, ADR 10 may facilitate collection ofphysiological parameter data for patient monitoring, patient diagnosis,or therapy evaluation.

In the illustrated embodiment, ADR 10 is attached to the abdomen ofpatient 12. However, ADR 10 may be attached to patient 12 at anylocation, including the chest, back, pelvis, or head of the patient. Insome embodiments, ADR 10 may be attached to patient 12 at a locationproximate to an implantable medical device. In other embodiments, ADRmay be implanted proximate to an intended location for an implantablemedical device, in order to approximate the sensing that may be laterperformed by the implantable medical device.

ADR 10 may be attached to patient 12 by an adhesive. In otherembodiments, the ADR may be attached to patient by a band, belt, orsutures. The invention is not limited to any particular attachmentmechanism, although an adhesive may allow ADR 10 to be attached topatient 12 more comfortably and unobtrusively than other attachmentmechanisms

Further, the sensors may be located on or within a housing of the ADR,which may allow ADR 10 to sense physiological parameters without leads.Additionally, ADR 10 may be waterproof and small relative to existingdata recorders with similar functional capabilities. The waterproofness,size, lack of leads, and adhesive attachment of ADR 10 may contribute tothe comfort and unobtrusiveness of ADR 10, allowing patient 12 to wearADR substantially continuously over an extended period of time andduring a variety of daily activities.

FIG. 2 is perspective view of wearable ADR. In the illustratedembodiment, ADR 10 includes a patch 14, which may be a bandage orbandage-like, for attachment to patient 12. Patch 14 may include anadhesive layer (not shown in FIG. 2) for attachment of ADR 10 topatient. Patch 14 may be made of any of a variety of conformable,flexible materials with relatively low durometers. Patch 14 may be madefrom any materials known for use in bandages. As examples, bandage 14may be made from polymers, elastomers, foams, hydrogels,polytetraflouroethylene (PTFE), expanded PTFE, silicone, silicone gel,or the like. Including such materials in patch 14 may improve thecomfort and unobtrusiveness of ADR 10 when worn by patient 12.

FIG. 3A is a cross-sectional side-view further illustrating wearable ADR10, the cross section taken along line A-A′ of FIG. 2. As illustrated inFIG. 3A, ADR 10 includes a housing 16 within patch 14. Housing 16 may bemade of relatively rigid materials, and may be waterproof. Examplematerials for housing 16 may include plastics, polymers, metals,polyvinylchloride, or the like.

In the illustrated example, the sensors of ADR 10 include two electrodes18A and 18B (collectively “electrodes 18”) formed on or within housing16. ADR 10 may include any number of electrodes 18. ADR 10 mayadditionally or alternatively include other sensors on or within housing16, such as accelerometers or optical sensors. Sensors may be formed onhousing 16 to, for example, facilitate contact of the sensor withtissue, e.g., skin, of the patient.

An adhesive layer 20 for attaching ADR 10 to patient 12 is alsoillustrated in FIG. 3A. Adhesive layer 20 may include any of a varietyof contact adhesives known to be usable for bandages or otherapplications in which an object is adhesively attached to the skin of apatient. A removable backing layer 22 may be attached to adhesive 20prior to application of ADR 10 to patient 12, e.g., during shipment andother handling of the ADR, and removed for attachment of ADR 10 topatient.

In some embodiments, ADR 10 may be shipped and handled in a no orlow-power shipment mode to reduce consumption of an internal powersource, e.g., battery, of the ADR prior to use. ADR 10 may detectremoval of backing layer 22 as an indication that the ADR is about to beattached to the patient for sensing physiological parameters of thepatient. In response to detecting removal of the backing layer, ADR 10may enter a powered or fully-functional mode to allow sensing ofphysiological parameters.

For example, as illustrated in FIG. 3A, backing layer 22 may include amagnet 24. ADR 10 may detect removal of the backing layer by detectingremoval of the magnet from a proximate position to ADR 10. As anexample, removal of the backing layer, and thus the magnet, may actuatea switch within ADR 10 to couple an internal power source of the ADR toother components of the ADR, e.g., to power on the ADR.

However, the invention is not limited to embodiments in which aremovable element is a backing layer or magnet. For example, in someembodiments, an ADR may be coupled to a removable circuit component. Insuch embodiments, the ADR may detect an open-circuit or impedance changeresulting from removal of the component.

In embodiments in which ADR 10 includes a posture sensor, e.g., amulti-axis accelerometer, removal of backing layer 22 and magnet 24, orsome other removable element, may also cause ADR 10 to calibrate thesensor. One example technique for calibrating the sensor involves havingthe patient assume a predetermined posture, e.g., upright, so that ADR10 may associate a current sensed posture with the predeterminedposture. ADR 10 may calibrate the posture sensor immediately afterremoval of backing layer 22 and magnet 24, which in some embodiment isimmediately after a power on or power up, or may wait a predeterminedtime after removal to allow ADR 10 to be attached to patient 12, andpatient 12 to assume the predetermined posture.

FIG. 3A also illustrates a thickness 26C of housing 16. A smallerthickness 26C may allow greater comfort and unobtrusiveness of ADR 10when worn by patient 12. Thickness 26C may be less than approximately1.5 centimeters, less than approximately 1 centimeter, or approximately0.6 centimeters. These example thicknesses 26C of housing 16 may improvethe comfort and unobtrusiveness of ADR 10 when worn by patient 12.

FIG. 3B is a cross-sectional top-view further illustrating wearable ADR10 of FIG. 1, the cross section taken along line B-B′ of FIG. 3A. Asillustrated in FIG. 3B, housing 16 may also have a length 26A and width26B, smaller dimensions for which may also allow greater comfort andunobtrusiveness of ADR 10 when worn by patient 12. Length 26A may beapproximately 4.5 centimeters, and width 26B may be approximately 2.2centimeters. A volume of housing 16 may be less than approximately 20cubic centimeters, less than approximately 10 cubic centimeters, orapproximately 6.3 cubic centimeters. These example dimensions forhousing 16 may allow greater comfort and unobtrusiveness of ADR 10 whenworn by patient 12. A length 28A and width 28B for bandage are alsoillustrated in FIG. 3B. Length 28A and width 28B may be within a rangefrom approximately 6 centimeters to approximately 10 centimeters.

FIG. 3C is a cross-sectional side-view illustrating another examplewearable ADR 11. ADR 11 is substantially similar to ADR 10 (FIG. 3A),and includes many substantially similar and like-numbered components asADR 10. Such components are not discussed in detail with respect to FIG.3C. However, unlike ADR 10, backing layer 22 does not include magnet 24for ADR 11. Instead, magnet 24 is located within a different layer 23,which is attached to ADR 11, e.g., by an adhesive layer 21. In someembodiments, layer 23 is not required, and magnet 24 is attacheddirectly to ADR 10 by adhesive layer 21 or other means.

The location of magnet 24 on ADR 11 may facilitate attachment of ADR 11to patient 12 prior to power up or power on, and prior to posture sensorcalibration. In particular, a clinician or patient 12 may remove backinglayer 22, and attach ADR 11 to patient 12 either before or after thepatient has assumed the predetermined posture. With ADR 11 attached andpatient 12 in the predetermined posture, the user may remove backinglayer 23 and magnet 24. In response to the removal, ADR 11 may power upor power on, and then calibrate the posture sensor in response to theremoval and the powering up or on, either substantially immediately, orafter a predetermined delay.

Further, the invention is not limited to the example embodimentsillustrated in FIGS. 3A and 3C. For example, in some embodiments, afirst magnet may be locating in backing layer 22, removal of whichpowers on or powers up the ADR, after which second magnet attached tothe ADR may be removed to cause the ADR to calibrate the posture sensor.Additionally, as discussed above, a removable element is not limited toa magnet, need not be included within a backing layer, and need not beattached to an ADR by an adhesive.

Moreover, the invention is not limited to embodiments in which removalof an element causes power on/up or sensor calibration. In otherembodiments, either or both of power on/up or posture sensor calibrationmay occur in response to different signals provided to an ADR. Forexample, movement of magnet not attached to the ADR proximate to theADR, or “tapping” the ADR, which may be sensed via a piezoelectricelement or accelerometer, may be cause the ADR to power on/up orcalibrate a sensor.

FIG. 4 is block diagram illustrating an example system 30 that includesADR 10. As shown in the example of FIG. 4, ADR 10 includes a variety ofcomponents that may be located on or within housing 16, such as aprocessor 32, switch 33, memory 34, sensors 36, internal power source38, e.g., battery 38, and power control module 39. Sensors 36 mayinclude one or more sensors as described below.

Processor 32 controls the operation of ADR 10 as instructed throughinstructions stored in memory 34. Processor 32 receives data produced bysensors 36 and conditions, filters, or samples the data before storingthe processed data in memory 32. Processor 32 may include amicroprocessor, a controller, a digital signal processor (DSP), anapplication specific integrated circuit (ASIC), a field-programmablegate array (FPGA), discrete logic circuitry, or the like. Memory 34 mayinclude any volatile, non-volatile, magnetic, optical, or electricalmedia, such as a random access memory (RAM), read-only memory (ROM),non-volatile RAM (NVRAM), electrically-erasable programmable ROM(EEPROM), flash memory, and the like. Memory 34 stores at least aportion of the data generated by sensors 36, as determined by processor32. In some embodiments, memory 34 may also store program instructionsthat, when executed by processor 32, determine the operation of sensors36, the processing of generated data, or the transfer of data to anotherdevice. In addition, memory 34 may store information related to anyother function described herein.

Sensors 36 may include one or more individual sensors, where the sensorsmay include multiple of one type of sensor. Each of sensors 36 generatesa signal as a function of one or more physiological or activityparameters of patient 12. Processor 32 or circuitry within sensors 36may condition or processes the data generated by each sensor. Forexample, ADR 10 may include one or more analog to digital converters toconvert analog signals generated by sensors 36 into digital signalsusable by processor 32, as well as suitable filter and amplifiercircuitry. Further, as illustrated in FIG. 4, sensors 36 may be locatedwithin housing 16 or on the surface of housing 16. In other embodiments,sensors 36 may include a sensor tethered to housing 16 through a lead orother similarly insulated conductive wire.

As discussed above, exemplary physiological parameters of patient 12that may be monitored by ADR 10 to determine values of one or morequality of life metrics include activity level, posture, heart rate,respiration rate, blood oxygen saturation, partial pressure of oxygenwithin blood, EEG, ECG, partial pressure of oxygen within cerebrospinalfluid, muscular activity (EMG), temperature (such as skin temperature),and arterial blood flow of patient 12. Sensors 36 may be of any typeknown in the art capable of generating a signal as a function of one ormore of these parameters. For example, sensors 36 may include one ormore thermocouples or thermistors to detect temperature of patient 12.Alternatively, an optical sensor that generates a signal as a functionof the partial pressure of oxygen within the cerebrospinal fluid may beincluded in sensors 36.

In some embodiments, in order to determine one or more sleep qualitymetric values, processor 32 determines when patient 12 is attempting tofall asleep by detecting that patient 12 is laying down and has notmoved for a predetermine period of time. This posture detection may alsobe coupled with an ECG measurement or breathing rate measurement. ECGand or respiration rate may signal a sleeping event. For example,processor 32 may identify the time that patient begins attempting tofall asleep based on the generated data from sensors 36.

In other embodiments, ADR 10 may include one or more sensors 36 thatgenerate a signal as a function of patient activity. For example,sensors 36 may include one or more accelerometers, gyros, mercuryswitches, or bonded piezoelectric crystals that generate a signal as afunction of patient activity, e.g., body motion, footfalls or otherimpact events, and the like. The plurality of accelerometers, gyros, ormagnetometers may be oriented orthogonally to generate signals whichindicate the posture of patient 12. Processor 36 may identify a timewhen the activity level of patient 12 falls below a threshold activitylevel value stored in memory 34, and may determine whether the activitylevel remains substantially below the threshold activity level value fora threshold amount of time stored in memory 34. In other words, patient12 remaining inactive for a sufficient period of time may indicate thatpatient 12 is attempting to fall asleep. If processor 32 determines thatthe threshold amount of time is exceeded, processor 32 may identify thetime at which the activity level fell below the threshold activity levelvalue as the time that patient 12 began attempting to fall asleep.

Processor 32 may also determine when patient 12 wakes up to store thelength of time patient 12 was asleep. Sensors 36 may also determine theamount of activity during patient sleep, as more activity may berepresentative of lower quality of sleep for patient 12. This data maybe useful to a physician and patient 12 as asking the patient to ratequality of sleep is highly subjective and not very accurate due to thefact that the patient was sleeping at the time.

When sensors 36 include accelerometers, for example, that are aligned inthis manner, e.g., a multi-axis accelerometer, processor 32 may monitorthe magnitude and polarity of DC components of the signals generated bythe accelerometers to determine the orientation of patient 12 relativeto the Earth's gravity, e.g., the posture of patient 12. In particular,the processor 32 may compare the DC components of the signals torespective threshold values stored in memory 34 to determine whetherpatient 12 is or is not recumbent. Further information regarding use oforthogonally aligned accelerometers to determine patient posture may befound in a commonly assigned U.S. Pat. No. 5,593,431, which issued toTodd J. Sheldon.

Processor 32 may periodically determine the posture of patient 12, andmay store indications of the determined postures within memory 34. WhereADR 10 includes a plurality of orthogonally aligned accelerometerslocated on or within the trunk of patient 12, as illustrated in FIG. 1,processor 32 may be able to periodically determine whether patient is,for example, upright or recumbent, e.g., lying down.

Processor 32 may identify postures and posture transitions by comparingthe signals generated by the accelerometers to one or more respectivethreshold values. For example, when patient 12 is upright, a DCcomponent of the signal generated by one of the plurality oforthogonally aligned accelerometers may be substantially at a firstvalue, e.g., high or one, while the DC components of the signalsgenerated by the others of the plurality of orthogonally alignedaccelerometers may be substantially at a second value, e.g., low orzero. When patient 12 becomes recumbent, the DC component of the signalgenerated by one of the plurality of orthogonally aligned accelerometersthat had been at the second value when the patient was upright maychange to the first value, and the DC components of the signalsgenerated by others of the plurality of orthogonally alignedaccelerometers may remain at or change to the second value. Processor 32may compare the signals generated by such sensors to respectivethreshold values stored in memory 34 to determine whether they aresubstantially at the first or second value, and to identify when thesignals change from the first value to the second value.

Processor 32 may determine an activity level based on one or more of theaccelerometer signals by sampling the signals and determining a numberof activity counts during the sample period. For example, processor 32may compare the sample of a signal generated by an accelerometer to oneor more amplitude thresholds stored within memory 34, and may identifyeach threshold crossing as an activity count. Where processor 32compares the sample to multiple thresholds with varying amplitudes,processor 32 may identify crossing of higher amplitude thresholds asmultiple activity counts. Using multiple thresholds to identify activitycounts, processor 32 may be able to more accurately determine the extentof patient activity for both high impact, low frequency and low impact,high frequency activities. Processor 32 may store the determined numberof activity counts in memory 34 as an activity level. In someembodiments, processor 46 may apply a digital filter, that passes a bandof the accelerometer signal from approximately 0.1 Hz to 10 Hz, e.g.,the portion of the signal that reflects patient activity.

Processor 32 may identify postures and record activity levelscontinuously or periodically, e.g., one sample of the signals output bysensors 36 every minute or continuously for ten minutes each hour.Further, processor 32 need not identify postures and record activitylevels with the same frequency. For example, processor 32 may identifypostures less frequently then activity levels are determined.

In some embodiments, processor 32 limits recording of postures andactivity levels to relevant time periods, i.e., when patient 12 is awakeor likely to be awake, and therefore likely to be active. In otherembodiments, processor 32 may maintain a real-time clock, and may recordposture events based on the time of day indicated by the clock, e.g.,processor 32 may limit posture event recording to daytime hours.Alternatively, processor 32 may wirelessly interact with a real-timeclock within a patient programmer.

In some embodiments, processor 32 may monitor one or more physiologicalparameters of patient 12 via signals generated by sensors 36, and maydetermine when patient 12 is attempting to sleep or asleep based on thephysiological parameters. For example, processor 32 may determine whenpatient 12 is attempting to sleep by monitoring the posture of patient12 to determine when patient 12 is recumbent.

In order to determine whether patient 12 is asleep, processor 32 maymonitor any one or more physiological parameters that discernibly changewhen patient 12 falls asleep, such as activity level, heart rate, EEG,ECG morphological features, respiration rate, respiratory volume, bloodpressure, blood oxygen saturation, partial pressure of oxygen withinblood, muscular activity and tone, skin temperature, arterial bloodflow, eye motion, and galvanic skin response. Processor 32 mayadditionally or alternatively monitor the variability of one or more ofthese physiological parameters, such as heart rate and respiration rate,which may discernible change when patient 12 is asleep. In someembodiments, processor may determine a probability of sleep for each ofa plurality of parameters, and combine the probabilities to determine anoverall probability that indicates whether the patient is asleep.Further details regarding monitoring physiological parameters toidentify when a patient is attempting to sleep and when the patient isasleep may be found in a commonly-assigned and co-pending U.S. patentapplication by Kenneth Heruth and Keith Miesel, entitled “DETECTINGSLEEP,” which was assigned Attorney Docket No. 1023-360U502 and filedMar. 16, 2005, and is incorporated herein by reference in its entirety.

Although described above with reference to an exemplary embodiment inwhich sensors 36 include accelerometers, the sensors may include any ofa variety of types of sensors that generate signals as a function ofpatient posture and/or activity. For example, sensors 36 may includeorthogonally aligned gyros or magnetometers that generate signals thatindicate the posture of patient 12.

Other sensors 36 that may generate a signal that indicates the postureof patient 12 include electrodes that generate an electromyogram (EMG)signal, or bonded piezoelectric crystals that generate a signal as afunction of contraction of muscles. Such sensors 36 may be placed on thelegs, buttocks, abdomen, or back of patient 12, as described above. Thesignals generated by such sensors when placed at these locations mayvary based on the posture of patient 12, e.g., may vary based on whetherthe patient is standing, sitting, or lying down.

Other sensors 36 that output a signal as a function of patient activitymay include one or more bonded piezoelectric crystals, mercury switches,or gyros that generate a signal as a function of body motion, footfallsor other impact events, and the like. Additionally or alternatively,sensors 36 may include one or more electrodes that generate anelectromyogram (EMG) signal as a function of muscle electrical activity,which may indicate the activity level of a patient. The electrodes maybe, for example, located on the legs, abdomen, chest, back or buttocksof patient 12 to detect muscle activity associated with walking,running, or the like. The electrodes may instead be located on the headto detect EEG signals or over the chest to detect ECG signals. Anyelectrical signals may detected by two or more electrodes included insensors 36. The electrodes may be attached to housing 16 or located onan adhesive portion of ADR 10 or tethered to housing 16.

Further, in some embodiments, processor 32 may monitor one or moresignals that indicate a physiological parameter of patient 12, which inturn varies as a function of patient activity. For example, processor 32may monitor a signal that indicates the heart rate, ECG morphology,respiration rate, subcutaneous temperature, or muscular activity of thepatient, and sensors 36 may include any known sensors that output asignal as a function of one or more of these physiological parameters.In such embodiments, processor 32 may periodically determine a heartrate, value of an ECG morphological feature, respiration rate, ormuscular activity level of patient 12 based on the signal. Thedetermined values of these parameters may be mean or median values. Insome embodiments, respiratory rate may also be used to show signs ofsleep apnea or other sleep disorders that cause patient 12 to stopbreathing for a certain period of time.

In alternative embodiments, processor 32 compares a determined value ofsuch a physiological parameter to one or more thresholds or a look-uptable stored in memory to determine a number of activity counts, andstores the determined number of activity counts in memory 34 as adetermined activity level. In other embodiments, processor 32 may storethe determined physiological parameter value as a determined activitylevel. The use of activity counts, however, may allow processor 32 todetermine an activity level based on a plurality of signals generated bya plurality of sensors 36. For example, processor 32 may determine afirst number of activity counts based on a sample of an accelerometersignal and a second number of activity counts based on a heart ratedetermined from an electrogram signal at the time the accelerometersignal was sampled. Processor 32 may determine an activity level bycalculating the sum or average, which may be a weighted sum or average,of first and second activity counts. In some embodiments, processor 32may not interact with previously stored data in order to reduce theamount of power drained from battery 38.

Sensors 36 may also include optical pulse oximetry sensors or Clarkdissolved oxygen sensors located within, as part of a housing of, oroutside of ADR 10, which generate signals as a function of blood oxygensaturation and blood oxygen partial pressure respectively.

In some embodiments, sensors 36 may include one or more external flowsensors positioned to generate a signal as a function of arterial bloodflow. A flow sensor may be, for example, an electromagnetic, thermalconvection, ultrasonic-Doppler, or laser-Doppler flow sensor. Further,in some external medical device embodiments of the invention, sensors 36may include one or more electrodes positioned on the skin of patient 12to generate a signal as a function of galvanic skin response.

Processor 32 may also detect arousals and/or apneas that occur whenpatient 12 is asleep based on one or more of the above-identifiedphysiological parameters. For example, processor 32 may detect anarousal based on an increase or sudden increase in one or more of heartrate, heart rate variability, respiration rate, respiration ratevariability, blood pressure, or muscular activity as the occurrence ofan arousal. Processor 32 may detect an apnea based on a disturbance inthe respiration rate of patient 12, e.g., a period with no respiration.Memory 34 may store thresholds used by processor 46 to detect arousalsand apneas. Processor 32 may determine, as a sleep quality metric value,the number of apnea events and/or arousals during a night.

Further, in some embodiments, processor 32 may determine which sleepstate patient 12 is in during sleep, e.g., REM, S1, S2, S3, or S4, basedon one or more of the monitored physiological parameters. In particular,memory 34 may store one or more thresholds for each of sleep states, andprocessor 32 may compare physiological parameter or sleep probabilitymetric values to the thresholds to determine which sleep state patient12 is currently in. Processor 32 may determine, as sleep quality metricvalues, the amounts of time per night spent in the various sleep states.As discussed above, inadequate time spent in deeper sleep states, e.g.,S3 and S4, is an indicator of poor sleep quality. Consequently, in someembodiments, processor 32 may determine an amount or percentage of timespent in one or both of the S3 and S4 sleep states as a sleep qualitymetric.

Processor 32 may store any of these metric values and otherphysiological parameter data determined based on signals received fromsensors 36 in memory 34. A computing device 40 may retrieve thephysiological parameter data from ADR 10 via a wired or wirelessconnection with the ADR, e.g., a wired or wireless serial dataconnection. Computing device 40 may include a user interface 44, such asa display, to present the retrieved physiological parameter data to auser, and a memory 46 to store the data.

Processor 42 may format or otherwise process the data prior topresentation. For example, processor 42 may arrange the data as one ormore timing diagrams, histograms, or the like. Processor 42 and memory46 may include components similar to those listed above for processor 32and memory 34, although the components within computing device 40 maygenerally by larger and more complex than those in the ADR.

The internal power source, e.g., battery 38, of ADR 10 may be primary orrechargeable, and may have, as an example, a Lithium Ion chemistry.Under the direction of processor 32, a power control module 39 maycontrol recharging of battery 38, monitoring parameters of the batteryfor end-of-life estimations, and delivery of power from battery 38 tothe other components of ADR 10. Power control module 39 may includecircuitry known in the art for such functions, and may also include aswitch to couple battery to the other components when a magnet 24 isremoved from proximity to ADR 10, as described above.

Also, ADR 10 may include an additional switch 33 or other detectionelement for detecting removal of a magnet or some other signal toperform a posture sensor calibration. Switch 33 may be included in ADR10 in embodiments in which the ADR is shipped or otherwise provided to auser in a fully-powered state, or in which separate magnets, removableelements, or other signals are used to control power on/up and sensorcalibration.

Further, as shown in FIG. 4, system 30 may also include a dockingstation 50, which may facilitate the data communication between ADR 10and computing device 40. The data connections between ADR and dockingstation, and docking station and computing device, may be wired orwireless serial or parallel data connections. Docking station 50 alsomay include or be coupled to an external power source 52, such as abattery or wall-outlet, for recharging battery 38 of ADR 10, or poweringADR 10. Docking station may, but need not, physically receive ADR or aportion thereof for transfer of data, recharging, or powering.

FIG. 5 is a flow diagram illustrating an example method for operatingwearable ADR 10 according to the invention. Prior to use, e.g., duringmanufacturing, housing 16 with components therein or thereon is sealedinto a patch, and a layer with a magnet is attached to an adhesive layer(60). ADR 10 detects the presence of the magnet (62), and remains in a“shelf mode,” e.g., a no or low-power mode, so long as magnet is present(64). When ADR 10 detects removal of the magnet (62), ADR “wakes up,”e.g., enters a powered or fully-functional mode (66). Removal of themagnet and back layer may be for attachment of the ADR to the patient,or after such attachment, as discussed above.

ADR 10 may then calibrate posture sensing, in embodiments with posturesensors, and start recording physiological parameter data (68).Calibrating posture sensing may include determining the orientation ofthe ADR with respect to the patient. For example, the user, e.g.,clinician, may have patient 12 assume a predetermined position, e.g.,lying down while face up, or upright, prior to removal of magnet 24.Processor 32 of ADR 10 may calibrate posture sensing by correlating thecurrent output of the multi-axis accelerometers of sensors 36, e.g.,upon power on/up or a predetermined delay thereafter, with thepredetermined posture.

In some embodiments, as discussed above, the indication to calibrateposture sensing may be separate from the indication to power up or on,e.g., may be a later “start recording” indication. The “start recording”indication may take the form of removal of a magnet or backing layer, ora predetermined number or pattern of “taps” on ADR 10 by a user.Processor 32 of ADR 10 may detect such an indication via, for example,accelerometers included within sensors 36.

In some embodiments, as discussed above, the internal power source,e.g., battery 38, of ADR 10 may be rechargeable. In other embodiments,the internal power source need not be recharged. Further, in someembodiments, ADR 10 may monitor physiological parameters and recordphysiological parameter data (74) until a time determined based on theestimated end-of-service (EOS) of the power source (72). In suchembodiments, processor 32 of ADR 10 may monitor a parameter, e.g.,voltage or current, associated with the power source, and estimate theEOS based on the parameter.

When the end of monitoring time determined based on the estimated EOS isreached, processor 32 may end data recording, and prompt a patient tomake the data recorder available for retrieval of information based onthe estimation. For example, the patient may be prompted to return theADR to a physician. The patient or physician may remove ADR 10 frompatient 12 (76), remove housing 16 from bandage 14 (78), and placehousing 16 into docking station 50 (80). Computing device 40 may thenretrieve stored physiological parameter information from ADR 10 viadocking station 50, as described above (82). In some embodiments,docking station 50 may power ADR during data transfer. Powering ADR 10via docking station 50 during data transfer may be advantageous,particularly if ADR 10 has reached the EOS of its internal power sourceduring data collection.

FIG. 6 is a graph illustrating an example relationship between postureand activity. At any moment in time, a patient can be thought of asbeing somewhere in an activity continuum. ADR 10 may use sensors 36 todetermine how active a person is, this level of activity could be usedto determine therapy efficacy and quality of life or provide closed looptherapy adjustment. Based solely on activity, different activity statescould be determined such as short distance walking, long distancewalking, jogging, and running If posture is gathered in addition toactivity data, additional activity states such as lying down andstanding can be determined to increase the resolution of the activitycontinuum as shown in FIG. 6. ADR 10 may record the number or length oftimes patient 12 spends above various activity thresholds, or withinvarious activity states or postures, as activity and posture metrics,e.g., as physiological parameter data.

FIG. 7 is a timing diagram illustrating example physiological parameterdata collected by a wearable ADR, such as ADR 10. As shown in FIG. 7,ADR 10 may use sensors 36, e.g., multi-axis accelerometers, to identifywhen patient 12 has reached or exceeded particular gross activity levelthresholds, and is within particular postures. Processor 32 of ADR 10may store such information as activity and posture metrics,respectively, for later retrieval by a computing device.

FIG. 8 is a histogram illustrating an example presentation ofphysiological parameter data collected by a wearable ADR, such as ADR10. The example presentation of FIG. 8 may be, for example, displayed bycomputing device 40 via user interface 44 after processing physiologicalparameter data received from ADR 10. FIG. 8 illustrates a single exampleposture metric, percent of time in upright positions, over time.

As shown in FIG. 8, ADR 10 may be used to evaluate the posture metricduring a variety of time periods, including a baseline evaluationpre-therapy, an evaluation during a trialing period, and an evaluationpost-implant. Reviewing such a histogram may provide a user with anobjective indication of the effectiveness of the therapy during trialingprior to implanting a medical device, and an objective evaluation of thecontinuing effectiveness of the therapy post implant. Such informationmay also allow the user to identify any gradual or sudden changes in thecondition of the patient over time.

FIG. 9 is a conceptual diagram illustrating the wearable ADR 10 andpatient 12 of FIG. 1 in conjunction with an implantable medical device(IMD) 92 implanted within the patient. In the illustrated embodiment,IMD 92 is an implantable neurostimulator that delivers stimulationtherapy to a spinal cord 90 of patient 12, e.g., spinal cord stimulation(SCS), via leads 94A and 94B (collectively, “leads 94”). However, theinvention is not limited to any particular therapy, or to implanteddevices. For example, ADR 10 may be used to record physiologicalparameter data during delivery of therapy by an external device, such asan external trial neurostimulator or other external trial device. Asdiscussed above, ADR 10 may record physiological parameter data duringdelivery of therapy by IMD 92 to evaluate the effectiveness of thedelivery of therapy by the IMD.

IMD 92 may deliver therapy according to therapy parameter sets. Instimulation embodiments, a therapy parameter set may include voltage orcurrent pulse amplitude, as well as pulse width and rate. Further, thetherapy parameter sets may include respective combinations of electrodes(not shown) carried by leads 94. For other therapies, the content of aparameter set may be different. For example, a parameter set for a drugpump may include a titration rate and duty cycle. As illustrated in FIG.9, in addition to computing with ADR 10 to retrieve physiologicalparameter data, as discussed above, computing device 40 may communicatewith IMD 92, e.g., via wireless telemetry. Computing device 40 maycommunicate with IMD 92 to, for example, retrieve information regardingthe therapy parameter sets used by the IMD to delivery therapy topatient 12.

FIG. 10 is a flow diagram illustrating example use of physiologicalparameter data collected by a wearable ADR, such as ADR 10. As shown inFIG. 10, the ADR may record physiological parameter data for one or moresensed physiological parameters during delivery of therapy, as discussedherein (91). The data may be processed by the ADR, the therapydelivering medical device, a special purpose programming device or othercomputing device, or some other device, by application of an algorithmor other analog or digital signal processing techniques (93). Forexample, the processing may yield values of one or more sleep quality,activity, or posture metrics, as described herein.

The therapy delivering medical device, independently or as controlled beanother device, may deliver therapy based on such processed data (99).Physiological parameter data from the ADR, whether or not furtherprocessed, may be used by the medical device, or another devicecontrolling the medical device, to provide closed loop therapy. Further,whether alone or combined with subjective information from the patient,such information may be used to control or inform decisions regardingother therapies (101), or to objectively evaluate the efficacy of thetherapy delivered to the patient by the medical device during datacollection by the ADR (97). Subjective information from the patient mayinclude information logged into a patient diary through interaction ofthe patient with a programming or other computing device, which mayprompt the patient to respond to queries regarding quality of life ortherapy efficacy. Objective therapy evaluation based on physiologicalparameter data from an ADR is described in greater detail below.

FIG. 11 is a flow diagram illustrating an example technique forassociating physiological parameter data collected by a wearable ADRwith therapy data relating to therapy delivered by a medical device. Theexample technique of FIG. 11 may be employed by, for example, computingdevice 40.

Computing device 40 may retrieve physiological parameter data from ADR10, as described above (100). IMD 92 or an associated programming devicemay record therapy changes over time as therapy data. Computing device40 may also retrieve therapy data from IMD 92 or the programming device(102). Each therapy parameter change may represent a change to a newparameter set or a parameter set selected from a plurality ofpreprogrammed sets. Computing device 40 may associate the physiologicalparameter data and therapy data according to time, e.g., may associateeach parameter set with physiological parameter data collecting duringdelivery of therapy according to the parameter set (104). The computingdevice may present therapy data, e.g., parameter sets, and associatedphysiological parameter data to a user (104). The computing device may,for example present therapy data and associated physiological parameterdata to the user in tabular or graphical form via a display. The usermay objectively evaluate the relative efficacy of the therapy changes orparameter sets based on the physiological parameter data associated withthe change or set.

FIG. 12 is a flow diagram illustrating an example technique fordetermining quality of life metric values for each of a plurality oftherapy parameter sets used by a medical device that delivers therapybased on physiological parameter data collected by a wearable ADR. Thetechnique of FIG. 12 may be employed by, for example, computing device40. According to the illustrated technique, the computing deviceretrieves physiological parameter data from the ADR and therapy datafrom the IMD, and associates the physiological parameter data withwhichever therapy parameter set was active during it collection, asdescribed above with reference to the FIG. 11 (100-104). The computingdevice may then further determine respective values for one or morequality of life metrics for each of the therapy parameter sets based onthe physiological parameter data associated with the therapy parametersets (110). The computing device may present a plurality of parametersets and associated values of any one or more metrics to a user (112).

The quality of life metrics may be sleep quality metrics, activitymetrics, or posture metrics. In general, the computing device determinessleep quality metrics by determining whether the patient is asleep orwithin a particular sleep state. The computing device may determine whenthe patient was asleep by analyzing data from the ADR, including datafrom any of the sensors described above as being useful to determinewhether the patient is asleep or within a particular sleep state. Thecomputing device may determine values for activity and posture metricsby analyzing recorded data from any sensors of ADR described above asgenerating signals indicative of gross motor activity, cardiovascularactivity, muscular activity or posture. The computing device or ADR maycompare such signals to thresholds to determine activity levels andpostures.

Sleep efficiency and sleep latency are example sleep quality metrics forwhich a computing device may determine values. Sleep efficiency may bemeasured as the percentage of time while the patient is attempting tosleep that the patient is actually asleep. Sleep latency may be measuredas the amount of time between a first time when the patient beginsattempting to fall asleep and a second time when the patient fallsasleep, and thereby indicates how long a patient requires to fallasleep.

The time when the patient begins attempting to fall asleep may bedetermined in a variety of ways. For example, the ADR or medical devicemay receive an indication from the patient that the patient is trying tofall asleep. In other embodiments, the computing device may determinewhen the patient is attempting to fall asleep based on the activitylevel or posture of the patient as indicated by physiological parameterdata received from the ADR

Other sleep quality metrics that may be determined include total timesleeping per day, the amount or percentage of time sleeping duringnighttime or daytime hours per day, and the number of apnea and/orarousal events per night. In some embodiments, which sleep state thepatient is in, e.g., rapid eye movement (REM), or one of the nonrapideye movement (NREM) states (S1, S2, S3, S4) may be determined based onphysiological parameters monitored by the medical device, and the amountof time per day spent in these various sleep states may be a sleepquality metric. Because they provide the most “refreshing” type ofsleep, the amount of time spent in one or both of the S3 and S4 sleepstates, in particular, may be determined as a sleep quality metric.

An activity metric value may be, for example, a mean or median activitylevel, such as an average number of activity counts per unit time. Inother embodiments, the computing device may choose an activity metricvalue from a predetermined scale of activity metric values based oncomparison of a mean or median activity level to one or more thresholdvalues. The scale may be numeric, such as activity metric values from1-10, or qualitative, such as low, medium or high activity. In someembodiments, the computing device compares each activity level indicatedby the data from the ADR that has been associated with a therapyparameter set with the one or more thresholds, and determinespercentages of time above and/or below the thresholds as one or moreactivity metric values for that therapy parameter set. In otherembodiments, each activity level associated with a therapy parameter setis compared with a threshold, and an average length of time thatconsecutively determined activity levels remain above the threshold isdetermined as an activity metric value for that therapy parameter set.

A posture metric value may be, for example, an amount or percentage oftime spent in a posture while a therapy parameter set is active, e.g.,average amount of time over a period of time, such as an hour, that apatient was within a particular posture. In some embodiments, a posturemetric value may be an average number of posture transitions over aperiod of time, e.g., an hour, that a particular therapy parameter setswas active.

FIGS. 13-15 are conceptual diagrams illustrating presentation of variousexample quality of life metric values associated with therapy parametersets to a user. More particularly, FIG. 13 illustrates an example listor table 120 of therapy parameter sets and associated sleep qualitymetric values that may be presented to a clinician by, as examplescomputing device 40 or a specialized programming device for a therapydelivering medical device. Each row of example list 120 includes anidentification of one of therapy parameter sets, the parameters of theset, and a representative value for one or more sleep quality metricsassociated with the identified therapy parameter set, such as sleepefficiency, sleep latency, or both. The example list 120 includesrepresentative values for sleep efficiency, sleep latency, and “deepsleep,” e.g., the average amount of time per night spent in either ofthe S3 and S4 sleep states.

FIG. 14 illustrates an example list 130 of therapy parameter sets andassociated activity metric values that may be presented to a clinicianby, as examples computing device 40 or a specialized programming devicefor a therapy delivering medical device. Each row of example list 130includes an identification of one of the therapy parameter sets, theparameters of the therapy parameter set, and values associated with thetherapy parameter set for each of two illustrated activity metrics.

The activity metrics illustrated in FIG. 14 are a percentage of timeactive, and an average number of activity counts per hour. The computingor other device may determine the average number of activity counts perhour for one of the illustrated therapy parameter sets by identifyingthe total number of activity counts associated with the parameter setand the total amount of time that the IMD or other therapy-deliveringmedical device was using the parameter set. The computing or otherdevice may determine the percentage of time active for one of parametersets by comparing activity levels over time as indicated by the datarecorded by ADR to an “active” threshold, and determining the percentageof activity levels above the threshold. As illustrated in FIG. 14, thecomputing device may also compare each activity level to an additional,“high activity” threshold, and determine a percentage of activity levelsabove that threshold.

Similarly, FIG. 15 illustrates an example list 140 of therapy parametersets and associated posture metric values that may be presented by thecomputing device or some other device. The posture metrics illustratedin FIG. 15 are a percentage of time upright, and an average number ofposture transitions per hour. The computing device may determine theaverage number of posture transitions per hour for one of theillustrated therapy parameter sets by identifying the total number ofposture transitions associated with the parameter set and the totalamount of time that an IMD or other therapy-delivering medical devicewas using the parameter set. The computing device may determine thepercentage of time upright for one of the parameter sets as thepercentage of the total time that the therapy parameter set was in usethat the patient was in an upright position, as indicated by theposture-related sensor data from the ADR.

The present application is related to, and incorporates herein byreference, each of the following pending U.S. Patent Applications:

-   -   1) U.S. patent application entitled “Collecting Sleep Quality        Information Via A Medical Device”, Ser. No. 10/826,925, filed on        Apr. 15, 2004.    -   2) U.S. patent application entitled “Collecting Sleep Quality        Information Via A Medical Device”, Ser. No. 11/081,811, filed on        Mar. 16, 2005.    -   3) U.S. patent application entitled “Collecting Posture        Information to Evaluate Therapy”, Ser. No. 10/826,926, filed on        Apr. 15, 2004.    -   4) U.S. patent application entitled “Collecting Posture        Information to Evaluate Therapy”, Ser. No. 11/081,872, filed on        Mar. 16, 2005.    -   5) U.S. Patent Application entitled “Detecting Sleep”, Ser. No.        10/825,964, filed on Apr. 15, 2004.    -   6) U.S. Patent Application entitled “Detecting Sleep”, Ser. No.        11/081,786, filed on Mar. 16, 2005.    -   7) U.S. patent application entitled “Collecting Activity        Information to Evaluate Therapy”, Ser. No. 10/825,965, filed on        Apr. 15, 2004.    -   8) U.S. patent application entitled “Collecting Activity        Information to Evaluate Therapy”, Ser. No. 11/081,785, filed on        Mar. 16, 2005.    -   9) U.S. patent application entitled “Collecting Activity and        Sleep Quality Information via a Medical Device”, Ser. No.        10/825,955, filed on Apr. 15, 2004.    -   10) U.S. patent application entitled “Collecting Activity and        Sleep Quality Information via a Medical Device”, Ser. No.        11/081,857, filed on Mar. 16, 2005.    -   12) U.S. patent application entitled “Controlling Therapy Based        on Sleep Quality”, Ser. No. 10,825,953, filed on Apr. 15, 2004.    -   13) U.S. patent application entitled “Controlling Therapy Based        on Sleep Quality”, Ser. No. 11/081,155, filed on Mar. 16, 2005.    -   14) U.S. patent application entitled “Sensitivity Analysis for        Selecting Therapy Parameter Sets”, Ser. No. 11/081,873, filed        Mar. 16, 2005.    -   15) U.S. patent application entitled “Collecting Posture and        Activity Information to Evaluate Therapy”, Ser. No. 11/106,051,        filed Apr. 14, 2005.    -   16) U.S. Provisional Application entitled “Correlating a        Non-Polysomnographic Physiological Parameter Set with Sleep        States”, Ser. No. 60/686,317, filed Jun. 1, 2005.

An ADR according to the invention may be used in any of the systemsdescribed in the incorporated applications to sense any of thephysiological parameters described therein. Further, the ADR, atherapy-delivering medical device, specialized programming device, orother computing device may determine values for any of the sleep,activity, or posture metrics described therein. The ADR may be usedaccording to the techniques described in the above-identifiedapplications to sense physiological parameters as instead or in additionto any device described therein as sensing physiological parameters.

As indicated above, movement disorders, such as tremor, Parkinson'sdisease, multiple sclerosis, and spasticity may affect the overallactivity level of a patient. Movement disorders are also characterizedby irregular, uncontrolled and generally inappropriate movements, e.g.,tremor or shaking, particularly of the limbs. In addition to using thesensors described above to sense the overall activity level of amovement disorder patient, some embodiments of the invention may usesuch sensors to detect the types of inappropriate movements associatedwith the movement disorder. For example, accelerometers, piezoelectriccrystals, or EMG electrodes located one the trunk or limbs of a patientmay be able to detect inappropriate movements such as tremor or shaking

Embodiments of the invention may periodically determine the level orseverity of such movements based on the signals output by such sensorsto evaluate the quality of a patient's life or a movement disordertherapy. For example, a processor of such a system may determine afrequency or amount of time that such movements exceeded a threshold forthis purpose.

Another activity-related movement disorder symptom that is relativelyspecific to Parkinson's disease is “gait freeze.” Gait freeze may occurwhen a Parkinson's patient is walking Gait freeze refers to a relativelysudden inability of a Parkinson's patient to take further steps. Gaitfreeze is believed to result from a neurological failure and, morespecifically, a failure in the neurological signaling from the brain tothe legs.

Some embodiments of the invention may additionally identify gait freezeevents based on the signals output by sensors as discussed above. Forexample, embodiments may detect a relatively sudden cessation ofactivity associated with a gait event based on the output ofaccelerometers, piezoelectric crystals, EMG electrodes, or other sensorsthat output signals based on footfalls or impacts associated with, forexample, walking When experiencing a gait freeze event, a patient may“rock” or “wobble” while standing in place, as if attemptingunsuccessfully to move. Some embodiments, may monitor any of the sensorsthat output signals as a function of posture discussed above, such as a3-axis accelerometer, to detect the minor, rhythmic changes in postureassociated with rocking or wobbling. Such embodiments may detect a gaitfreeze event as when it occurs based on one or more of the posture oractivity sensors. Some embodiments may confirm that a relatively suddencessation of activity is in fact a gait freeze event based on rocking orwobbling indicated by posture sensors.

Some embodiments may detect a gait freeze prior to onset. For example,the sensors may include EMG or EEG electrodes, and a processor maydetect a gait freeze prior to onset based on irregular EMG or EEGactivity. EMG signals, as an example, demonstrate irregularity justprior to a freezing episode, and a processor may detect thisirregularity as being different from the EMG signals typicallyassociated with walking In other words, a walking patient may exhibitnormal EMG pattern in the legs, which may be contrasted with EMGactivity and timing changes that precede freezing.

In general, EMG signals from right and left leg muscles include aregularly alternating rhythm pattern that characterizes normal gait.When the “timing” of the pattern fails, there is no longer a regularrhythm, and a gait freeze may result. Accordingly, a processor maydetect irregularity, variability, or asymmetry, e.g., within and betweenright and left leg muscles, in one or more EMG signals, and may detectan oncoming gait freeze prior to occurrence based on the detection. Insome embodiments, the processor may compare the EMG signals to one ormore thresholds to detect gait freeze. Comparison to a threshold may,for example, indicate an absolute value or increase in irregularity,variability, asymmetry that exceeds a threshold, indicating an oncominggait freeze. In some embodiments, thresholds may be determined based onEMG signal measurements made when the patient is walking normally.

Whether or not gait freeze is detected prior to or during occurrence,embodiments be used may evaluate quality of life or therapy based on thegait freeze, e.g., total number of gait freeze events for the therapyparameter set, or an average number of gait freeze events over a periodof time.

Systems according to the invention may include any of a variety ofmedical devices that deliver any of a variety of therapies to treatmovement disorders, such as DBS, cortical stimulation, or one or moredrugs. Baclofen, which may or may not be intrathecally delivered, is anexample of a drug that may be delivered to treat movement disorders.Systems may use the techniques of the invention described above toassociate any of the above-described sleep quality or activity metricswith therapies or therapy parameter sets for delivery of such therapies.In this manner, such systems may allow a user to evaluate the extent towhich a therapy or therapy parameter set is alleviating the movementdisorder by evaluating the extent to which the therapy parameter setimproves the sleep quality, general activity level, inappropriateactivity level, or number of gait freezes experienced by the patient.

Further, many of the ailments and symptoms described above, includingmovement disorders and chronic pain, may affect the gait of a patient.More particularly, such symptoms and ailments may result in, asexamples, an arrhythmic, asymmetric (left leg versus right leg), orunusually variable gait, or a gait with relative short stride lengths.Systems according to the invention may use sensors discussed above thatoutput signals as a function of activity, and particularly as a functionof footfalls or impacts, to monitor gait. For example, a processor ofsuch a system may periodically determine a value for asymmetry,variability, or stride length of gait, and use such values to evaluatequality of life, progression of a disease or symptom, or a therapydelivered to treat the symptom

Various embodiments of the invention have been described. However, oneof ordinary skill will appreciate that various modifications may be madeto the described embodiments without departing from the scope of theinvention. For example, the invention is not limited to divisions orattributions of functionality described above. Any one or more of anADR, therapy-delivering medical device, specialized programming device,computing device, or other device may perform the any of techniques ofthe invention, either alone or in combination.

The techniques described in this disclosure may be implemented inhardware, software, firmware or any combination thereof. For example,various aspects of the techniques may be implemented within one or moremicroprocessors, digital signal processors (DSPs), application specificintegrated circuits (ASICs), field programmable logic arrays (FPGAs), orany other equivalent integrated or discrete logic circuitry, as well asany combinations of such components. The term “processor” or “processingcircuitry” may generally refer to any of the foregoing logic circuitry,alone or in combination with other logic circuitry, or any otherequivalent circuitry, which may be located within one or more devices.

When implemented in software, the functionality ascribed to the systemsand devices described in this disclosure may be embodied as instructionson a computer-readable medium such as random access memory (RAM),read-only memory (ROM), non-volatile random access memory (NVRAM),electrically erasable programmable read-only memory (EEPROM), FLASHmemory, magnetic media, optical media, or the like. The instructions areexecuted to support one or more aspects of the functionality describedin this disclosure. These and other embodiments are within the scope ofthe following claims.

1. An external wearable ambulatory data recorder comprising: a housing; a sensor that generates a signal as a function of posture of a patient that wears the data recorder within the housing; a memory within the housing; a processor within the housing that receives the signal from the sensor, and stores posture data within the memory for the patient based on the signal; and an element removably attached to the data recorder, wherein the processor calibrates the sensor in response to removal of the element, wherein to calibrate the sensor in response to the removal of the element, the processor identifies an initial value of the signal in response to the removal of the element and associates the initial value with a predetermined posture, wherein the posture data corresponds to values of the signal correlated to the initial value of the signal associated with the predetermined posture.
 2. The data recorder of claim 1, further comprising a switch within the housing, wherein removal of the element actuates the switch to power on the data recorder, and wherein the processor calibrates the sensor in response to the power on of the data recorder.
 3. The data recorder of claim 1, wherein the element comprises a magnet.
 4. The data recorder of claim 1, further comprising an adhesive, wherein the element comprises a removable layer attached to the adhesive.
 5. The data recorder of claim 4,-wherein the adhesive is configured to attach the housing to the patient.
 6. The data recorder of claim 4, wherein the removable layer includes a magnet.
 7. The data recorder of claim 4, further comprising a patch that contains the housing and includes the adhesive as a layer.
 8. The data recorder of claim 1, wherein the sensor comprises an accelerometer within the housing.
 9. The data recorder of claim 1, wherein the sensor comprises a plurality of orthogonally aligned accelerometers within the housing, each of the accelerometers generating a signal as a function of posture of the patient, and wherein the processor receives the plurality of signals, and stores posture data within the memory for the patient based on the signals.
 10. The data recorder of claim 1, further comprising a plurality of sensors that sense a plurality of physiological parameters of the patient, wherein the plurality of sensors are located at least one of on or within the housing.
 11. The data recorder of claim 10, wherein the plurality of sensors comprises at least one electrode formed on the housing.
 12. The data recorder of claim 10, wherein the plurality of sensors sense at least one of activity, heart activity, brain activity, muscle activity, respiration, temperature, blood oxygen saturation, blood pressure, blood flow, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, tissue oxygenation, or galvanic skin response of the patient.
 13. The data recorder of claim 10, wherein of the processor records at least one of an activity level, a posture, an electrocardiogram, an electroencephalogram, an electromyogram, a heart rate, a heart rate variability, a respiration rate, or a respiration rate variability for the patient within the memory.
 14. The data recorder of claim 1, wherein the housing is waterproof.
 15. The data recorder of claim 1, wherein a thickness of the housing is less than approximately 1.5 centimeters.
 16. The data recorder of claim 1, wherein a volume of the housing is less than approximately 20 cubic centimeters.
 17. An external wearable ambulatory data recorder comprising: a housing; means within the housing for sensing posture of a patient that wears the data recorder and generating posture data; means within the housing for storing the posture data; an element removably attached to the housing; and means for calibrating the posture sensing means in response to removal of the element, wherein the means for calibrating comprises means for identifying an initial sensed posture in response to the removal and means for associating the initial sensed posture with a predetermined posture, wherein the posture data corresponds to sensed postures correlated to the initial sensed posture associated with the predetermined posture.
 18. The data recorder of claim 17, further means within the housing for powering on the data recorder in response to removal of the element, wherein the calibrating means calibrates the sensor in response to the power on of the data recorder.
 19. An external wearable ambulatory data recorder comprising: a sensor that senses posture of a patient wearing the external ambulatory data recorder; a memory; and a processor, wherein the processor is programmed to: store posture data from the sensor within the memory, detect removal of an element from the data recorder, identify an initial sensed posture of the patient in response to the detection of removal, and calibrate posture sensing in response to the removal by associating the initial sensed posture with a predetermined posture, wherein storing posture data comprises storing posture data corresponding to sensed postures of the patient correlated to the initial sensed posture associated with the predetermined posture.
 20. The data recorder of claim 19, wherein calibrating posture sensing comprises: powering on the data recorder in response to the removal, and calibrating posture sensing in response to the power on of the data recorder.
 21. The data recorder of claim 20, wherein detecting removal of an element comprises detecting removal of a magnet.
 22. The data recorder of claim 19, wherein the processor is programmed to sense a plurality of physiological parameters of the patient via the sensor or one or more additional sensors of the data recorder.
 23. The data recorder of claim 22, wherein sensing a plurality of physiological parameters comprises sensing a plurality of activity, heart activity, brain activity, muscle activity, respiration, temperature, blood oxygen saturation, blood pressure, blood flow, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, tissue oxygenation, or galvanic skin response of the patient.
 24. The data recorder of claim 19, further comprising a housing, wherein the sensor, the memory, and the processor are within the housing. 